For the detection of opponent race vehicles, we employ a neural network that uses preprocessed point clouds (Section REF ) as input. Specifically, we select the two-stage PointRCNN [1]}, which ranked at the top of the KITTI Benchmark at the time of selection [2]}. To fit our needs for detecting race vehicles, we modify this network as described in the following. First, we adapt the network configuration to enable a 360 horizontal FoV. Additionally, we move the reference system of the detections from the front camera (default KITTI Benchmark) to the vehicle rear axle. Since there is only one type of race vehicle to detect, the network only has to predict one class. Finally, we manually fine-tune parameters, such as detection thresholds, for best performance in our use case.
